# IberFire -- a detailed creation of a spatio-temporal dataset for wildfire risk assessment in Spain

arXiv: https://arxiv.org/abs/2505.00837 · HTML: https://mlsanity.com/paper/2505.00837/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 33 references that are in the map.
- First posted on arXiv 1 May 2025.

## Builds on (most cited first, 3 of 3)

- [Mesogeos: A multi-purpose dataset for data-driven wildfire modeling in the Mediterranean](https://mlsanity.com/paper/2306.05144/) (2023)
- [Deep Learning Methods for Daily Wildfire Danger Forecasting](https://mlsanity.com/paper/2111.02736/) (2021)
- [Spain on Fire: A novel wildfire risk assessment model based on image satellite processing and atmospheric information](https://mlsanity.com/paper/2306.05045/) (2023)

## Built on by (most cited first, 1 of 1)

- [PyroStack: A Multi-Band Spatio-Temporal Sub-Daily Dataset for Wildfires in the United States](https://mlsanity.com/paper/2609.36315/) (2026)

## Abstract (arXiv)

Wildfires pose a threat to ecosystems, economies and public safety, particularly in Mediterranean regions such as Spain. Accurate predictive models require high-resolution spatio-temporal data to capture complex dynamics of environmental and human factors. To address the scarcity of fine-grained wildfire datasets in Spain, we introduce IberFire: a spatio-temporal dataset with 1 km x 1 km x 1-day resolution, covering mainland Spain and the Balearic Islands from December 2007 to December 2024. IberFire integrates 120 features across eight categories: auxiliary data, fire history, geography, topography, meteorology, vegetation indices, human activity and land cover. All features and processing rely on open-access data and tools, with a publicly available codebase ensuring transparency and applicability. IberFire offers enhanced spatial granularity and feature diversity compared to existing European datasets, and provides a reproducible framework. It supports advanced wildfire risk modelling via Machine Learning and Deep Learning, facilitates climate trend analysis, and informs fire prevention and land management strategies. The dataset is freely available on Zenodo to promote open research and collaboration.